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coal-price-forecast

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Add granger_causality_testing.ipynb

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@@ -199,13 +199,456 @@
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  },
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  "cell_type": "code",
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- "execution_count": null,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "metadata": {},
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  "outputs": [],
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  "source": [
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- "df = pd.merge(df_apple[['Date', 'Adj Close']], df_walmart[['Date', 'Adj Close']], on='Date', how='right').rename(columns = {'Adj Close_x':'apple', 'Adj Close_y':'walmart'})\n",
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- "df = df.merge(df_tesla[['Date', 'Adj Close']], on='Date', how='right').rename(columns={'Adj Close':'tesla'})"
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  ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
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  ],
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  "metadata": {
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 13,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "df = pd.merge(df_apple[['Date', 'Price']], df_walmart[['Date', 'Price']], on='Date', how='right').rename(columns = {'Adj Close_x':'apple', 'Adj Close_y':'walmart'})\n",
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+ "df = df.merge(df_tesla[['Date', 'Price']], on='Date', how='right').rename(columns={'Adj Close':'tesla'})"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ " <th></th>\n",
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+ " <td>182.52</td>\n",
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+ " <td>175.56</td>\n",
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+ " <td>191.97</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>01/01/2024</td>\n",
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+ " <td>184.40</td>\n",
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+ " <td>165.25</td>\n",
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+ " <td>187.29</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>12/01/2023</td>\n",
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+ " <td>192.53</td>\n",
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+ " <td>157.65</td>\n",
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+ " <td>248.48</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>11/01/2023</td>\n",
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+ " <td>189.95</td>\n",
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+ " <td>155.69</td>\n",
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+ " <td>240.08</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>10/01/2023</td>\n",
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+ " <td>170.77</td>\n",
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+ " <td>163.41</td>\n",
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+ " <td>200.84</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>159</th>\n",
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+ " <td>11/01/2010</td>\n",
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+ " <td>11.11</td>\n",
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+ " <td>54.09</td>\n",
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+ " <td>2.36</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>160</th>\n",
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+ " <td>10/01/2010</td>\n",
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+ " <td>10.75</td>\n",
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+ " <td>54.17</td>\n",
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+ " <td>1.46</td>\n",
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+ " <tr>\n",
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+ " <th>161</th>\n",
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+ " <td>1.36</td>\n",
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+ " <th>162</th>\n",
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+ " <td>8.68</td>\n",
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+ " <td>1.30</td>\n",
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+ " <td>07/01/2010</td>\n",
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+ " <td>9.19</td>\n",
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+ "4 10/01/2023 170.77 163.41 200.84\n",
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+ ".. ... ... ... ...\n",
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+ "163 07/01/2010 9.19 51.19 1.33\n",
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+ "\n",
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+ "[164 rows x 4 columns]"
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+ "execution_count": 14,
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+ "metadata": {},
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+ "df"
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+ "cell_type": "code",
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+ "execution_count": 15,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "df['Date'] = pd.to_datetime(df['Date'])"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ " <thead>\n",
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+ " <td>182.52</td>\n",
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+ " <td>175.56</td>\n",
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+ " <td>191.97</td>\n",
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+ " <th>1</th>\n",
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+ " <td>2024-01-01</td>\n",
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+ " <td>184.40</td>\n",
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+ " <td>165.25</td>\n",
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+ " <td>187.29</td>\n",
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+ " </tr>\n",
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+ " <th>2</th>\n",
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+ " <td>2023-12-01</td>\n",
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+ " <td>192.53</td>\n",
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+ " <td>157.65</td>\n",
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+ " <td>248.48</td>\n",
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+ " </tr>\n",
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+ " <th>3</th>\n",
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+ " <td>2023-11-01</td>\n",
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+ " <td>189.95</td>\n",
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+ " <td>155.69</td>\n",
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+ " <td>240.08</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>170.77</td>\n",
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+ " <td>163.41</td>\n",
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+ " </tr>\n",
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+ " <th>159</th>\n",
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+ " <td>2010-11-01</td>\n",
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+ " <td>11.11</td>\n",
439
+ " <td>54.09</td>\n",
440
+ " <td>2.36</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>160</th>\n",
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+ " <td>2010-10-01</td>\n",
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+ " <td>10.75</td>\n",
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+ " <td>54.17</td>\n",
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+ " <td>1.46</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>161</th>\n",
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+ " <td>2010-09-01</td>\n",
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+ " <td>10.13</td>\n",
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+ " <td>53.52</td>\n",
454
+ " <td>1.36</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>162</th>\n",
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+ " <td>2010-08-01</td>\n",
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+ " <td>8.68</td>\n",
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+ " <td>50.14</td>\n",
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+ " <td>1.30</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>163</th>\n",
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+ " <td>2010-07-01</td>\n",
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+ " <td>9.19</td>\n",
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+ " <td>51.19</td>\n",
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+ " <td>1.33</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "<p>164 rows × 4 columns</p>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " Date Price_x Price_y Price\n",
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+ "0 2024-02-01 182.52 175.56 191.97\n",
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+ "1 2024-01-01 184.40 165.25 187.29\n",
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+ "2 2023-12-01 192.53 157.65 248.48\n",
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+ "3 2023-11-01 189.95 155.69 240.08\n",
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+ "4 2023-10-01 170.77 163.41 200.84\n",
482
+ ".. ... ... ... ...\n",
483
+ "159 2010-11-01 11.11 54.09 2.36\n",
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+ "160 2010-10-01 10.75 54.17 1.46\n",
485
+ "161 2010-09-01 10.13 53.52 1.36\n",
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+ "162 2010-08-01 8.68 50.14 1.30\n",
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+ "163 2010-07-01 9.19 51.19 1.33\n",
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+ "\n",
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+ "[164 rows x 4 columns]"
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+ ]
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+ },
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+ "execution_count": 16,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ "source": [
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+ "df"
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 17,
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  "metadata": {},
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  "outputs": [],
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  "source": [
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+ "df = df.set_index('Date').rename_axis('company', axis=1)"
 
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  ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 18,
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+ "outputs": [
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+ {
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+ "data": {
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+ " }\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th>company</th>\n",
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+ " <th>Price_x</th>\n",
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+ " <th>Price</th>\n",
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+ " </tr>\n",
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+ " <th>Date</th>\n",
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+ " </thead>\n",
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+ " <tr>\n",
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+ " <th>2024-02-01</th>\n",
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+ " <td>182.52</td>\n",
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+ " <td>175.56</td>\n",
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+ " <td>191.97</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2024-01-01</th>\n",
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+ " <td>184.40</td>\n",
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+ " <td>165.25</td>\n",
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+ " <td>187.29</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2023-12-01</th>\n",
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+ " <td>192.53</td>\n",
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+ " <td>157.65</td>\n",
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+ " <td>248.48</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2023-11-01</th>\n",
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+ " <td>189.95</td>\n",
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+ " <td>155.69</td>\n",
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+ " <td>240.08</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2023-10-01</th>\n",
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+ " <td>170.77</td>\n",
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+ " <td>163.41</td>\n",
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+ " <td>200.84</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>...</th>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2010-11-01</th>\n",
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+ " <td>11.11</td>\n",
587
+ " <td>54.09</td>\n",
588
+ " <td>2.36</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2010-10-01</th>\n",
592
+ " <td>10.75</td>\n",
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+ " <td>54.17</td>\n",
594
+ " <td>1.46</td>\n",
595
+ " </tr>\n",
596
+ " <tr>\n",
597
+ " <th>2010-09-01</th>\n",
598
+ " <td>10.13</td>\n",
599
+ " <td>53.52</td>\n",
600
+ " <td>1.36</td>\n",
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+ " </tr>\n",
602
+ " <tr>\n",
603
+ " <th>2010-08-01</th>\n",
604
+ " <td>8.68</td>\n",
605
+ " <td>50.14</td>\n",
606
+ " <td>1.30</td>\n",
607
+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2010-07-01</th>\n",
610
+ " <td>9.19</td>\n",
611
+ " <td>51.19</td>\n",
612
+ " <td>1.33</td>\n",
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+ " </tr>\n",
614
+ " </tbody>\n",
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+ "</table>\n",
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+ "<p>164 rows × 3 columns</p>\n",
617
+ "</div>"
618
+ ],
619
+ "text/plain": [
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+ "company Price_x Price_y Price\n",
621
+ "Date \n",
622
+ "2024-02-01 182.52 175.56 191.97\n",
623
+ "2024-01-01 184.40 165.25 187.29\n",
624
+ "2023-12-01 192.53 157.65 248.48\n",
625
+ "2023-11-01 189.95 155.69 240.08\n",
626
+ "2023-10-01 170.77 163.41 200.84\n",
627
+ "... ... ... ...\n",
628
+ "2010-11-01 11.11 54.09 2.36\n",
629
+ "2010-10-01 10.75 54.17 1.46\n",
630
+ "2010-09-01 10.13 53.52 1.36\n",
631
+ "2010-08-01 8.68 50.14 1.30\n",
632
+ "2010-07-01 9.19 51.19 1.33\n",
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+ "\n",
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+ "[164 rows x 3 columns]"
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+ ]
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+ },
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+ "outputs": [],
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  }
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  ],
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  "metadata": {